There is a method available by combining a method for extracting candidate regions of a physical object from an image, as a technique for detecting an object such as a moving person with its deformation and performing segmentation on an image including the object, and a method for applying a physical object model prepared in advance to the extracted candidate regions of the physical object. For example, Patent Reference 1 has disclosed the method for extracting a silhouette image of a physical object such as a person from images as a candidate region of the physical object and applying, to the extracted silhouette image, a model for the physical object whose region is parameterized in advance based on knowledge about the physical object. In this way, since a parameterized model can be applied to an object such as a moving person with its deformation, it becomes possible to detect the object and perform segmentation on a region of the object.
Furthermore, Non-Patent Reference 1 has disclosed the method for calculating distances between pixel value data in each image and pixel value data in other images, with images in which a fixed object is captured from angles as inputs, and performing nonlinear dimensionality reduction, with the distances as inputs, to  allow images captured from similar angles to be projected such that they are at a short distance in a two-dimensional space. Here, in comparison with the linear dimensionality reduction method such as conventional Principal Component Analysis (PCA), it is shown that reduction to much lower dimension is possible and, further, that handling data nonlinearly-distributed is also possible.    Patent Reference 1: Japanese Unexamined Patent Application Laid-Open Publication No. 8-214289.    Non-Patent Reference 1: Joshua Tenenbaum, Vin de Silva, John Langford, “A Global Geometric Framework for Nonlinear Dimensionality Reduction”, Science, VOL290, pp. 2319-2322, 22 Dec., 2000.